Stage 7b-i: dense safetensors Qwen3 load path

Adds the bf16/fp16 safetensors path alongside the existing GGUF
quantized one. The harness now dispatches by ModelSpec.quant:
- Some(_) → GGUF (pre-quantized, single-GPU only path, unchanged).
- None    → safetensors dense (new).

The dense path uses candle-transformers::models::qwen3::ModelForCausalLM
verbatim, fed via VarBuilder::from_mmaped_safetensors over the files
listed in `model.safetensors.index.json` (sharded layout) or the
single `model.safetensors` fallback. dtype is bf16 to match the
canonical Qwen3 HF distribution dtype. tokenizer.json is fetched from
the same repo (no -GGUF suffix to strip).

ModelArch gains a Qwen3Dense variant; the forward signature mirrors
QuantizedQwen3Weights (same `forward(&Tensor, offset)` → last-position
logits), so run_inference / run_inference_streaming just add a parallel
match arm — no shape changes downstream.

This is the foundation 7b-ii (ColumnParallel/RowParallel) builds on:
because the source is dense safetensors that can be byte-sliced per
rank, the TP work avoids the GGUF super-block alignment problem
entirely. Vanilla GGUF inference keeps working unchanged.

validate-neuron.sh learns the dense path: pass an empty third arg
(quant) and the script omits the `quant` field from the load
payload, triggering the dense dispatch. Example:
  script/validate-neuron.sh beast.hanzalova.internal Qwen/Qwen3-0.6B ''

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

crates/neuron/src/harness/candle.rs

@@ -11,9 +11,11 @@
 use anyhow::{Context, Result};
 use async_trait::async_trait;
 use candle_core::quantized::gguf_file;
-use candle_core::{Device, Tensor};
+use candle_core::{DType, Device, Tensor};
+use candle_nn::VarBuilder;
 use candle_transformers::generation::{LogitsProcessor, Sampling};
 use candle_transformers::models::quantized_qwen3::ModelWeights as QuantizedQwen3Weights;
+use candle_transformers::models::qwen3 as qwen3_dense;
 use cortex_core::harness::{Harness, HarnessHealth, ModelInfo, ModelSpec};
 use cortex_core::openai::{
     ChatCompletionChoice, ChatCompletionChunk, ChatCompletionRequest, ChatCompletionResponse,
@@ -46,11 +48,21 @@ pub struct LoadedModel {
     pub devices: Vec<u32>,
 }
 
-/// Architecture-specific weights. Stage 3 still supports only Qwen3
-/// quantized; Stage 8 broadens this to additional families and
-/// non-quantized variants.
+/// Architecture-specific weights.
+///
+/// - `Qwen3Quantized` — GGUF source, pre-quantized. Single-GPU only;
+///   TP sharding pre-quantized super-blocks is intractable. Stays the
+///   default for small models loaded via `Qwen/Qwen3-*-GGUF` and
+///   `unsloth/Qwen3-*-GGUF` repos.
+/// - `Qwen3Dense` — bf16 safetensors source. The path that supports
+///   TP (Stage 7b-ii+) because slicing dense weights by row/column
+///   under safetensors is mechanical. Used when `ModelSpec.quant` is
+///   None; intended target for Qwen3.6-27B etc.
+///
+/// Stage 8 broadens this to additional families.
 pub enum ModelArch {
     Qwen3Quantized(QuantizedQwen3Weights),
+    Qwen3Dense(qwen3_dense::ModelForCausalLM),
 }
 
 /// Repetition penalty applied to recently-generated tokens before
@@ -108,14 +120,172 @@ impl CandleHarness {
         Ok(Device::Cpu)
     }
 
-    /// Resolve a model spec to local GGUF and tokenizer file paths via
-    /// hf-hub. Downloads on first use; subsequent calls are cached.
-    async fn resolve_files(&self, spec: &ModelSpec) -> Result<(PathBuf, PathBuf)> {
+    /// Build an hf-hub API client pre-configured with the harness's
+    /// `hf_cache` (when one is set).
+    fn hf_api(&self) -> Result<hf_hub::api::tokio::Api> {
         let mut builder = hf_hub::api::tokio::ApiBuilder::new();
         if let Some(cache) = &self.hf_cache {
             builder = builder.with_cache_dir(cache.clone());
         }
-        let api = builder.build().context("build hf-hub API")?;
+        builder.build().context("build hf-hub API")
+    }
+
+    /// Resolve a dense (bf16/fp16 safetensors) model to its local file
+    /// paths.
+    ///
+    /// Handles both sharded repos (`model.safetensors.index.json` plus
+    /// several `model-*.safetensors`) and the single-file layout
+    /// (`model.safetensors`). Returns the safetensors paths in
+    /// arbitrary order — `VarBuilder` unifies them into one tensor view.
+    async fn resolve_dense_files(
+        &self,
+        spec: &ModelSpec,
+    ) -> Result<(PathBuf, PathBuf, Vec<PathBuf>)> {
+        let api = self.hf_api()?;
+        let repo = api.model(spec.model_id.clone());
+
+        let config_path = repo
+            .get("config.json")
+            .await
+            .with_context(|| format!("fetch config.json from {}", spec.model_id))?;
+        let tokenizer_path = repo
+            .get("tokenizer.json")
+            .await
+            .with_context(|| format!("fetch tokenizer.json from {}", spec.model_id))?;
+
+        // Prefer the sharded layout (most HF dense models > 5B ship it).
+        let safetensors_paths = match repo.get("model.safetensors.index.json").await {
+            Ok(index_path) => {
+                let index_text = std::fs::read_to_string(&index_path)
+                    .context("read model.safetensors.index.json")?;
+                let index: serde_json::Value = serde_json::from_str(&index_text)
+                    .context("parse model.safetensors.index.json")?;
+                let weight_map = index
+                    .get("weight_map")
+                    .and_then(|v| v.as_object())
+                    .ok_or_else(|| {
+                        anyhow::anyhow!("safetensors index missing weight_map object")
+                    })?;
+                let unique: std::collections::BTreeSet<String> = weight_map
+                    .values()
+                    .filter_map(|v| v.as_str().map(String::from))
+                    .collect();
+                let mut paths = Vec::with_capacity(unique.len());
+                for fname in unique {
+                    let p = repo
+                        .get(&fname)
+                        .await
+                        .with_context(|| format!("fetch sharded safetensors {fname}"))?;
+                    paths.push(p);
+                }
+                paths
+            }
+            Err(_) => {
+                // Single-file fallback.
+                let p = repo
+                    .get("model.safetensors")
+                    .await
+                    .context("fetch model.safetensors (single-file layout)")?;
+                vec![p]
+            }
+        };
+        Ok((config_path, tokenizer_path, safetensors_paths))
+    }
+
+    /// Resolve + load a GGUF (pre-quantized) Qwen3. Returns the
+    /// tokenizer.json path so the caller can construct the Tokenizer
+    /// uniformly across source formats.
+    async fn load_arch_gguf(
+        &self,
+        spec: &ModelSpec,
+        device: &Device,
+    ) -> Result<(PathBuf, ModelArch)> {
+        let (gguf_path, tokenizer_path) = self.resolve_files(spec).await?;
+        let device_for_load = device.clone();
+        let gguf_path_for_load = gguf_path.clone();
+        let model_id_for_log = spec.model_id.clone();
+        let arch = tokio::task::spawn_blocking(move || -> Result<ModelArch> {
+            tracing::info!(model = %model_id_for_log, path = ?gguf_path_for_load, "loading GGUF");
+            let mut file = std::fs::File::open(&gguf_path_for_load).context("open GGUF file")?;
+            let content = gguf_file::Content::read(&mut file)
+                .map_err(|e| anyhow::anyhow!("parse GGUF: {e}"))?;
+
+            let architecture = content
+                .metadata
+                .get("general.architecture")
+                .and_then(|v| v.to_string().ok().cloned())
+                .unwrap_or_default();
+            tracing::info!(architecture = %architecture, "GGUF architecture");
+
+            match architecture.as_str() {
+                "qwen3" => {
+                    let weights =
+                        QuantizedQwen3Weights::from_gguf(content, &mut file, &device_for_load)
+                            .map_err(|e| anyhow::anyhow!("from_gguf qwen3: {e}"))?;
+                    Ok(ModelArch::Qwen3Quantized(weights))
+                }
+                other => anyhow::bail!(
+                    "unsupported GGUF architecture '{other}'; quantized path only supports qwen3"
+                ),
+            }
+        })
+        .await
+        .context("blocking GGUF load task panicked")??;
+        Ok((tokenizer_path, arch))
+    }
+
+    /// Resolve + load a dense Qwen3 from safetensors. Uses
+    /// `candle-transformers::models::qwen3::ModelForCausalLM` and
+    /// builds a VarBuilder over the mmap'd safetensors files. dtype
+    /// is bf16 by default to match the HF distribution dtype for
+    /// recent Qwen3 family models; fall back to f16 if the device
+    /// doesn't support bf16.
+    async fn load_arch_dense(
+        &self,
+        spec: &ModelSpec,
+        device: &Device,
+    ) -> Result<(PathBuf, ModelArch)> {
+        let (config_path, tokenizer_path, safetensors_paths) =
+            self.resolve_dense_files(spec).await?;
+        let device_for_load = device.clone();
+        let model_id_for_log = spec.model_id.clone();
+
+        let arch = tokio::task::spawn_blocking(move || -> Result<ModelArch> {
+            tracing::info!(
+                model = %model_id_for_log,
+                shards = safetensors_paths.len(),
+                "loading dense Qwen3 from safetensors"
+            );
+            let cfg_text = std::fs::read_to_string(&config_path).context("read config.json")?;
+            let cfg: qwen3_dense::Config =
+                serde_json::from_str(&cfg_text).context("parse Qwen3 config.json")?;
+
+            // bf16 is the canonical Qwen3 distribution dtype. CUDA
+            // devices on Ada+ support it; Ampere also supports bf16
+            // natively. CPU candle handles bf16 via emulation.
+            let dtype = DType::BF16;
+            // SAFETY: VarBuilder::from_mmaped_safetensors mmaps the files;
+            // mutation of the underlying files by another process while
+            // we hold the mapping is UB. We trust that nothing else on
+            // the host modifies the HF cache files during a model's
+            // lifetime (hf-hub itself is immutable-by-design).
+            let vb = unsafe {
+                VarBuilder::from_mmaped_safetensors(&safetensors_paths, dtype, &device_for_load)
+                    .context("build VarBuilder over safetensors")?
+            };
+            let model = qwen3_dense::ModelForCausalLM::new(&cfg, vb)
+                .map_err(|e| anyhow::anyhow!("build Qwen3 dense model: {e}"))?;
+            Ok(ModelArch::Qwen3Dense(model))
+        })
+        .await
+        .context("blocking dense load task panicked")??;
+        Ok((tokenizer_path, arch))
+    }
+
+    /// Resolve a model spec to local GGUF and tokenizer file paths via
+    /// hf-hub. Downloads on first use; subsequent calls are cached.
+    async fn resolve_files(&self, spec: &ModelSpec) -> Result<(PathBuf, PathBuf)> {
+        let api = self.hf_api()?;
         let repo = api.model(spec.model_id.clone());
 
         let info = repo
@@ -406,13 +576,15 @@ impl Harness for CandleHarness {
 
         // Stage 7a-i scaffolds tensor-parallel worker subprocesses but
         // does not yet route inference through them. Refuse TP loads
-        // for now with a clear marker so the request surface is honest.
+        // for now with a clear marker so the request surface is honest;
+        // Stage 7b-iv replaces this bail with the TP dispatch.
         let tp_size = spec.tensor_parallel.unwrap_or(1);
         if tp_size > 1 {
             anyhow::bail!(
                 "tensor_parallel={tp_size} requested for '{}': TP worker \
-                 lifecycle is in place (Stage 7a-i) but TP-aware Qwen3 \
-                 inference lands in Stage 7b; single-GPU loads only for now",
+                 lifecycle + NCCL handshake are in place (Stage 7a) but \
+                 TP-aware Qwen3 inference orchestration lands in Stage \
+                 7b-iv; single-GPU loads only for now",
                 spec.model_id
             );
         }
@@ -420,44 +592,19 @@ impl Harness for CandleHarness {
         let devices = spec.devices.clone().unwrap_or_else(|| vec![0]);
         let device = Self::pick_device(&devices)?;
 
-        let (gguf_path, tokenizer_path) = self.resolve_files(spec).await?;
+        // Dispatch by source format: GGUF (pre-quantized, single-GPU
+        // only path) vs safetensors dense (bf16/fp16; the path that
+        // grows TP support). `spec.quant` is the signal — Some means
+        // the operator picked a quantized GGUF; None means dense.
+        let (tokenizer_path, arch) = if spec.quant.is_some() {
+            self.load_arch_gguf(spec, &device).await?
+        } else {
+            self.load_arch_dense(spec, &device).await?
+        };
 
         let tokenizer = Tokenizer::from_file(&tokenizer_path)
             .map_err(|e| anyhow::anyhow!("load tokenizer: {e}"))?;
 
-        // File I/O + GGUF parsing + tensor materialisation are CPU-bound,
-        // so run them on a blocking task to avoid stalling the runtime.
-        let device_for_load = device.clone();
-        let gguf_path_for_load = gguf_path.clone();
-        let model_id_for_log = spec.model_id.clone();
-        let arch = tokio::task::spawn_blocking(move || -> Result<ModelArch> {
-            tracing::info!(model = %model_id_for_log, path = ?gguf_path_for_load, "loading GGUF");
-            let mut file = std::fs::File::open(&gguf_path_for_load).context("open GGUF file")?;
-            let content = gguf_file::Content::read(&mut file)
-                .map_err(|e| anyhow::anyhow!("parse GGUF: {e}"))?;
-
-            let architecture = content
-                .metadata
-                .get("general.architecture")
-                .and_then(|v| v.to_string().ok().cloned())
-                .unwrap_or_default();
-            tracing::info!(architecture = %architecture, "GGUF architecture");
-
-            match architecture.as_str() {
-                "qwen3" => {
-                    let weights =
-                        QuantizedQwen3Weights::from_gguf(content, &mut file, &device_for_load)
-                            .map_err(|e| anyhow::anyhow!("from_gguf qwen3: {e}"))?;
-                    Ok(ModelArch::Qwen3Quantized(weights))
-                }
-                other => anyhow::bail!(
-                    "unsupported GGUF architecture '{other}'; Stage 3 only supports qwen3"
-                ),
-            }
-        })
-        .await
-        .context("blocking load task panicked")??;
-
         let loaded = Arc::new(LoadedModel {
             model_id: spec.model_id.clone(),
             arch: Arc::new(Mutex::new(arch)),
@@ -564,6 +711,13 @@ fn run_inference(
             let logits = logits.squeeze(0)?;
             sample_with_penalty(&logits, &generated, &mut logits_processor)?
         }
+        ModelArch::Qwen3Dense(model) => {
+            model.clear_kv_cache();
+            let input = Tensor::new(prompt_tokens, device)?.unsqueeze(0)?;
+            let logits = model.forward(&input, 0)?;
+            let logits = logits.squeeze(0)?;
+            sample_with_penalty(&logits, &generated, &mut logits_processor)?
+        }
     };
 
     if Some(next_token) == eos_id {
@@ -579,6 +733,12 @@ fn run_inference(
                 let logits = logits.squeeze(0)?;
                 sample_with_penalty(&logits, &generated, &mut logits_processor)?
             }
+            ModelArch::Qwen3Dense(model) => {
+                let input = Tensor::new(&[next_token], device)?.unsqueeze(0)?;
+                let logits = model.forward(&input, prompt_tokens.len() + index)?;
+                let logits = logits.squeeze(0)?;
+                sample_with_penalty(&logits, &generated, &mut logits_processor)?
+            }
         };
         if Some(next_token) == eos_id {
             return Ok((generated, "stop".into()));
@@ -635,6 +795,13 @@ fn run_inference_streaming(
             let logits = logits.squeeze(0)?;
             sample_with_penalty(&logits, &all_tokens, &mut logits_processor)?
         }
+        ModelArch::Qwen3Dense(model) => {
+            model.clear_kv_cache();
+            let input = Tensor::new(prompt_tokens, device)?.unsqueeze(0)?;
+            let logits = model.forward(&input, 0)?;
+            let logits = logits.squeeze(0)?;
+            sample_with_penalty(&logits, &all_tokens, &mut logits_processor)?
+        }
     };
 
     let emit_token = |all_tokens: &[u32], decoded_prefix: &mut String| -> Result<bool> {
@@ -683,6 +850,12 @@ fn run_inference_streaming(
                     let logits = logits.squeeze(0)?;
                     sample_with_penalty(&logits, &all_tokens, &mut logits_processor)?
                 }
+                ModelArch::Qwen3Dense(model) => {
+                    let input = Tensor::new(&[next_token], device)?.unsqueeze(0)?;
+                    let logits = model.forward(&input, prompt_tokens.len() + index)?;
+                    let logits = logits.squeeze(0)?;
+                    sample_with_penalty(&logits, &all_tokens, &mut logits_processor)?
+                }
             };
             if Some(next_token) == eos_id {
                 finish_reason = "stop".into();