214 lines
8.6 KiB
Markdown
214 lines
8.6 KiB
Markdown
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---
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tags:
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- gguf
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- llama.cpp
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- unsloth
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- llama
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- llama3.2
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- distillation
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- reasoning
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- fine-tuning
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license: llama3.2
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datasets:
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- angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
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language:
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- en
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base_model:
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- unsloth/Llama-3.2-3B-Instruct-bnb-4bit
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Llama 3.2 3B — Claude Reasoning Distill
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This model was a second attempt at reasoning distillation, with several fixes from the 1B run — but the core approach was still wrong.
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**1. Same root problem: SFT copies style, not capability** - GRPO is the right approach
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**2. Dataset truncation caused the stopping problem**
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The training dataset (`angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k`) averages ~1,954 tokens per example, with p90 assistant responses alone hitting ~1,760 tokens. Trained at `seq_len=2048`, a significant portion of examples were silently truncated — cutting off the `<|eot_id|>` end-of-turn token before it could be written.
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The model learned from many examples that responses don't need to end. This is a dataset fit problem, not a model problem.
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**3. Wrong EOS token at inference**
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Llama 3 has two EOS-like tokens. `tokenizer.eos_token_id` returns `128001` (`<|end_of_text|>`), but the model generates `128009` (`<|eot_id|>`) to end a turn. The default `model.generate()` call never passes `128009`, so generation runs until `max_new_tokens`. This compounds the truncation issue above.
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**Same Fix as 1B if you're using this model:**
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```python
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model.generate(
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input_ids=inputs,
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eos_token_id=[128001, 128009],
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max_new_tokens=512,
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repetition_penalty=1.3,
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no_repeat_ngram_size=6,
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)
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```
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For Ollama, add to your Modelfile:
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```
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PARAMETER stop "<|eot_id|>"
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PARAMETER stop "<|end_of_text|>"
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```
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---
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An updated attempt at distilling Claude Opus 4.6/4.7 reasoning traces into a small-form-factor model. The predecessor [Llama 3.2 1B Claude Opus Reasoning Distill](https://huggingface.co/codestrate/Llama3.2-1B-Claude-Opus-Reasoning-Distill) demonstrated that a 1B model could adopt `<think>` blocks but suffered from echolalia and a GSM8K regression. This run addresses the two root causes identified from that experiment:
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1. **Capacity** — 3B sits closer to the parameter floor where structured reasoning adoption is viable, as seen in models like [Gemma 4 E2B-IT](https://huggingface.co/google/gemma-4-E2B-it) and [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) (which has `<think>` baked into pretraining)
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2. **Token boundaries** — `<think>` and `</think>` are registered as special tokens (vocab 128256 → 128258) with trained embeddings, giving the model a hard mode boundary instead of treating them as plain text
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3. **Training on Reponses Only** - Unlike 1B run, I used the `train_on_responses_only` from `unsloth` to mask out user inputs to have a accuracy increase in multi-turn conversational fine tuning.
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> **Benchmarks will not be available.**
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---
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## Model Details
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| Field | Value |
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|---|---|
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| **Base model** | [`unsloth/Llama-3.2-3B-Instruct-bnb-4bit`](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct-bnb-4bit) |
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| **Model type** | Causal LM — LoRA adapter (PEFT) on Llama-3.2-3B-Instruct |
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| **Language** | English |
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| **License** | [Meta Llama 3.2 Community License](https://www.llama.com/llama3_2/license/) |
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| **Training framework** | Unsloth + TRL SFTTrainer |
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| **Hardware** | Tesla T4 (Kaggle) |
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| **Max sequence length** | 2048 |
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---
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## Intended Use
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Generating step-by-step reasoning traces (`<think>` blocks) followed by final answers across a broad range of instruction-following tasks. Useful for studying how reasoning distillation scales to sub-4B models and how registered thinking tokens affect small-model behaviour.
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**Not intended for:** production use, mathematical proofs requiring reliability, or replacing a larger reasoning model. Benchmark regressions vs base are expected until verified otherwise.
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---
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## How to Get Started
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### From the adapter
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The LoRA adapter is available separately — load it on top of the base model without downloading the full merged weights.
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> **Important:** load the tokenizer from the adapter directory, not the base model. The adapter tokenizer carries the correct 128258-token vocabulary with `<think>`/`</think>` baked in. Using the base model tokenizer (128256) will cause an embedding dimension mismatch.
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```python
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from unsloth import FastLanguageModel
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from transformers import AutoTokenizer, TextStreamer
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from peft import PeftModel
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ADAPTER_PATH = "codestrate/Llama3.2-3B-Claude-Reasoning-Distill"
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model, _ = FastLanguageModel.from_pretrained(
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model_name="unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
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load_in_4bit=True,
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max_seq_length=2048,
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)
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tokenizer = AutoTokenizer.from_pretrained(ADAPTER_PATH) # vocab=128258
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model.resize_token_embeddings(len(tokenizer))
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model = PeftModel.from_pretrained(model, ADAPTER_PATH)
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FastLanguageModel.for_inference(model)
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SYSTEM_PROMPT = "You are a helpful assistant. Think step by step inside <think>...</think> before giving your final answer."
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": "Write a Python function to check if a number is prime."},
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]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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_ = model.generate(
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input_ids=inputs,
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streamer=streamer,
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max_new_tokens=1024,
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temperature=0.7,
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min_p=0.1,
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repetition_penalty=1.3,
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no_repeat_ngram_size=6,
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use_cache=True,
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)
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```
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### From GGUF (Ollama / LM Studio)
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A Modelfile is included for Ollama. For direct use:
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```
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ollama run hf.co/codestrate/Llama3.2-3B-Claude-Reasoning-Distill:Q4_K_M
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```
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---
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## Training Details
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### Dataset
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[`angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k`](https://huggingface.co/datasets/angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k) — `instruct_train.jsonl` split (full instruct + reasoning, ~7,700 examples). Data already in OpenAI messages format; mapped directly through `apply_chat_template` with no additional preprocessing.
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The previous 1B run used only the `coding` + `math` categories (~2,000 examples). This run uses the full instruct split for broader coverage.
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### Hyperparameters
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| Parameter | Value |
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|---|---|
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| LoRA Rank / Alpha | 32 / 64 |
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| Target Modules | All |
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| Sequence Length | 2048 |
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| Effective Batch | 16 (2 × grad_accum 8) |
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| Steps | 904 (~2 epochs) |
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| Learning Rate | 1e-4 / cosine |
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| Warmup Steps | 50 |
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| Optimizer | adamw_8bit |
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| Weight Decay | 0.01 |
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| Precision | bfloat16 |
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### Loss Curve
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| Step | Loss | Step | Loss | Step | Loss |
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| 50 | 2.1372 | 350 | 1.8798 | 650 | 1.7567 |
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| 100 | 1.9597 | 400 | 1.8512 | 700 | 1.7530 |
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| 150 | 1.9251 | 450 | 1.8493 | 750 | **1.7391** |
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| 200 | 1.8972 | 500 | 1.7670 | 800 | 1.7709 |
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| 250 | 1.8891 | 550 | 1.7707 | 850 | 1.7401 |
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| 300 | 1.8738 | 600 | 1.7668 | 900 | 1.7598 |
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Drop: 2.14 → 1.74 (~0.40 absolute). Visible cross-epoch improvement at step ~452 (−0.082). Plateau reached in epoch 2 from step 750 — a third epoch would not have been beneficial on this dataset.
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---
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## Known Limitations
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- **Benchmarks not yet available** — results will be added when the evaluation runs complete
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- **Echolalia / repetition** — reduced vs the 1B run due to special token boundaries, but not eliminated; `repetition_penalty=1.3` and `no_repeat_ngram_size=6` are recommended at inference (needs more testing)
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- **System prompt required** — without the `<think>...</think>` contract in the system prompt, the model may not cleanly transition from reasoning block to final answer
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- **Not a production model** — a research artefact studying reasoning distillation at sub-4B scale
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---
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## Available Files
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| File | Format | Use |
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| `Llama-3.2-3B-Claude-Reasoning-Distill.Q4_K_M.gguf` | GGUF Q4_K_M | LM Studio / Ollama (recommended) |
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| `Llama-3.2-3B-Claude-Reasoning-Distill.Q8_0.gguf` | GGUF Q8 | Higher fidelity inference (near lossless; still lightweight)|
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| `Llama-3.2-3B-Claude-Reasoning-Distill.F16.gguf` | GGUF F16 | Full precision GGUF |
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| Adapter (`adapter_model.safetensors`) | LoRA adapter | PEFT inference / further fine-tuning |
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---
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## Framework Versions
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- Python 3.12.13
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- Unsloth 2026.5.8
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- PEFT 0.19.1
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- TRL 0.24.0
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- PyTorch 2.10.0+cu128
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- Transformers 4.47.1
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---
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*Predecessor: [Llama3.2-1B-Claude-Opus-Reasoning-Distill](https://huggingface.co/codestrate/Llama3.2-1B-Claude-Opus-Reasoning-Distill)*
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*Trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)*
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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