docs: update model card for Qwen3.8-27B-DeepResearch

README.md

@@ -1,100 +1,100 @@
----
-license: apache-2.0
-base_model: Qwen/Qwen3.8-27B
-tags:
-  - deep-research
-  - reasoning
-  - tool-use
-  - citations
-  - fact-checking
-  - science
-  - chain-of-thought
-  - qwen3.8
-language:
-  - en
-pipeline_tag: text-generation
----
-
-# Qwen3.8-27B-DeepResearch
-
-**Qwen3.8-27B-DeepResearch** is a specialized foundation model post-trained for **deep scientific reasoning, autonomous literature search, evidence synthesis, and citation-grounded question answering**.
-
-Post-trained using a **Hybrid Supervised Fine-Tuning (SFT) + Direct Preference Optimization (DPO)** pipeline built on top of `Qwen/Qwen3.8-27B`.
-
-## Key Capabilities
-
-1. **Structured Multi-Step Reasoning (`<thought>`)**: Employs deep chain-of-thought hypothesis testing, error correction, and mathematical derivation before formulating responses.
-2. **Strict Citation Grounding (`[1]`, `[2]`)**: Trained on multi-document retrieval benchmarks (AVeriTeC, ALCE, SciFact) to eliminate citation hallucination and tie claims directly to verified text spans.
-3. **Academic & Scientific Depth**: Excels in ingesting and synthesizing literature across physics, biomedicine, chemistry, and computer science.
-4. **General Helpfulness & Alignment**: Tuned with HelpSteer2 multi-attribute preferences to ensure high clarity, minimal verbosity, and zero sycophancy.
-
----
-
-## Prompt Format
-
-Qwen3.8-27B-DeepResearch follows the standard ChatML structure with an internal `<thought>` reasoning trace:
-
-```text
-<|im_start|>system
-You are a deeply researchful, rigorous, and helpful AI assistant. Always reason thoroughly inside <thought>...</thought> before delivering factual, structured responses with precise citations [1], [2].
-<|im_end|>
-<|im_start|>user
-Compare the mechanisms of mRNA-1273 and BNT162b2 lipid nanoparticles in cellular uptake, citing relevant literature.
-<|im_end|>
-<|im_start|>assistant
-<thought>
-Deconstruct lipid nanoparticle formulations:
-- mRNA-1273 (Moderna): SM-102, DSPC, cholesterol, PEG2000-DMG (50:10:38.5:1.5 molar ratio).
-- BNT162b2 (Pfizer/BioNTech): ALC-0315, DSPC, cholesterol, ALC-0159 (46.3:9.4:42.7:1.6 molar ratio).
-Examine endocytosis pathway: macropinocytosis vs clathrin-mediated uptake, endosomal escape kinetics.
-Formulate grounded response with clear citations.
-</thought>
-Both mRNA-1273 and BNT162b2 deliver nucleoside-modified mRNA encapsulated within ionizable lipid nanoparticles (LNPs) [1]...
-```
-
----
-
-## Inference with Transformers & vLLM
-
-### Using Hugging Face Transformers
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-
-base_model_id = "Qwen/Qwen3.8-27B"
-adapter_id = "honvert/Qwen3.8-27B-DeepResearch"
-
-tokenizer = AutoTokenizer.from_pretrained(adapter_id)
-base_model = AutoModelForCausalLM.from_pretrained(
-    base_model_id,
-    torch_dtype=torch.bfloat16,
-    device_map="auto"
-)
-model = PeftModel.from_pretrained(base_model, adapter_id)
-
-messages = [
-    {"role": "system", "content": "You are a deeply researchful and helpful AI assistant."},
-    {"role": "user", "content": "Synthesize recent findings on superconducting hydride materials at megabar pressures."}
-]
-
-text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95)
-print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
-```
-
----
-
-## Training Details
-
-* **Base Model**: `Qwen/Qwen3.8-27B` (Apache 2.0)
-* **Methodology**: LoRA (Rank 64, Alpha 128, all linear layers)
-* **Dataset Recipe**:
-  * 35% Deep Reasoning & Chain-of-Thought (`OpenThoughts3`, `OpenR1-Math`)
-  * 30% Web Search, Tool Execution & Citations (`AVeriTeC`, `ALCE`, `FactCheck`)
-  * 20% Scientific Paper Synthesis (`SciFact`, `PubMedQA`, `S2ORC`)
-  * 15% Multi-Attribute Helpfulness (`HelpSteer2`)
-* **License**: Apache 2.0
+---
+license: apache-2.0
+base_model: Qwen/Qwen3.8-27B
+tags:
+  - deep-research
+  - reasoning
+  - tool-use
+  - citations
+  - fact-checking
+  - science
+  - chain-of-thought
+  - qwen3.8
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Qwen3.8-27B-DeepResearch
+
+**Qwen3.8-27B-DeepResearch** is a specialized foundation model post-trained for **deep scientific reasoning, autonomous literature search, evidence synthesis, and citation-grounded question answering**.
+
+Post-trained using a **Hybrid Supervised Fine-Tuning (SFT) + Direct Preference Optimization (DPO)** pipeline built on top of `Qwen/Qwen3.8-27B`.
+
+## Key Capabilities
+
+1. **Structured Multi-Step Reasoning (`<thought>`)**: Employs deep chain-of-thought hypothesis testing, error correction, and mathematical derivation before formulating responses.
+2. **Strict Citation Grounding (`[1]`, `[2]`)**: Trained on multi-document retrieval benchmarks (AVeriTeC, ALCE, SciFact) to eliminate citation hallucination and tie claims directly to verified text spans.
+3. **Academic & Scientific Depth**: Excels in ingesting and synthesizing literature across physics, biomedicine, chemistry, and computer science.
+4. **General Helpfulness & Alignment**: Tuned with HelpSteer2 multi-attribute preferences to ensure high clarity, minimal verbosity, and zero sycophancy.
+
+---
+
+## Prompt Format
+
+Qwen3.8-27B-DeepResearch follows the standard ChatML structure with an internal `<thought>` reasoning trace:
+
+```text
+<|im_start|>system
+You are a deeply researchful, rigorous, and helpful AI assistant. Always reason thoroughly inside <thought>...</thought> before delivering factual, structured responses with precise citations [1], [2].
+<|im_end|>
+<|im_start|>user
+Compare the mechanisms of mRNA-1273 and BNT162b2 lipid nanoparticles in cellular uptake, citing relevant literature.
+<|im_end|>
+<|im_start|>assistant
+<thought>
+Deconstruct lipid nanoparticle formulations:
+- mRNA-1273 (Moderna): SM-102, DSPC, cholesterol, PEG2000-DMG (50:10:38.5:1.5 molar ratio).
+- BNT162b2 (Pfizer/BioNTech): ALC-0315, DSPC, cholesterol, ALC-0159 (46.3:9.4:42.7:1.6 molar ratio).
+Examine endocytosis pathway: macropinocytosis vs clathrin-mediated uptake, endosomal escape kinetics.
+Formulate grounded response with clear citations.
+</thought>
+Both mRNA-1273 and BNT162b2 deliver nucleoside-modified mRNA encapsulated within ionizable lipid nanoparticles (LNPs) [1]...
+```
+
+---
+
+## Inference with Transformers & vLLM
+
+### Using Hugging Face Transformers
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model_id = "Qwen/Qwen3.8-27B"
+adapter_id = "honvert/Qwen3.8-27B-DeepResearch"
+
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(base_model, adapter_id)
+
+messages = [
+    {"role": "system", "content": "You are a deeply researchful and helpful AI assistant."},
+    {"role": "user", "content": "Synthesize recent findings on superconducting hydride materials at megabar pressures."}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+---
+
+## Training Details
+
+* **Base Model**: `Qwen/Qwen3.8-27B` (Apache 2.0)
+* **Methodology**: LoRA (Rank 64, Alpha 128, all linear layers)
+* **Dataset Recipe**:
+  * 35% Deep Reasoning & Chain-of-Thought (`OpenThoughts3`, `OpenR1-Math`)
+  * 30% Web Search, Tool Execution & Citations (`AVeriTeC`, `ALCE`, `FactCheck`)
+  * 20% Scientific Paper Synthesis (`SciFact`, `PubMedQA`, `S2ORC`)
+  * 15% Multi-Attribute Helpfulness (`HelpSteer2`)
+* **License**: Apache 2.0