Text Generation
Transformers
Safetensors
qwen3
code
reasoning
lora-merged
livecodebench
conversational
text-generation-inference
Instructions to use modrill/code-think-q8b-20260908 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modrill/code-think-q8b-20260908 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="modrill/code-think-q8b-20260908") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("modrill/code-think-q8b-20260908") model = AutoModelForCausalLM.from_pretrained("modrill/code-think-q8b-20260908", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use modrill/code-think-q8b-20260908 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "modrill/code-think-q8b-20260908" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "modrill/code-think-q8b-20260908", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/modrill/code-think-q8b-20260908
- SGLang
How to use modrill/code-think-q8b-20260908 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "modrill/code-think-q8b-20260908" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "modrill/code-think-q8b-20260908", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "modrill/code-think-q8b-20260908" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "modrill/code-think-q8b-20260908", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use modrill/code-think-q8b-20260908 with Docker Model Runner:
docker model run hf.co/modrill/code-think-q8b-20260908
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license: apache-2.0
base_model: Qwen/Qwen3-8B-Base
tags:
- code
- reasoning
- lora-merged
- livecodebench
pipeline_tag: text-generation
library_name: transformers
---
# code-think-q8b-20260908
Research checkpoint: **Qwen/Qwen3-8B-Base** (`49e3418fbbbca6ecbdf9608b4d22e5a407081db4`) after V4 LoRA SFT on Qwen3-30B-A3B-Thinking-2507 traces, then merged to full weights.
This is a **research checkpoint, not a product**. Single-seed diagnostic numbers only. Do not treat DEV256 as a leaderboard claim.
License: **Apache-2.0**, inherited from [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) (verified from the local base `README.md`; Apache-2.0 text bundled from the Qwen3-4B-Base `LICENSE` file on disk).
## Base, teacher, and data
| | |
|---|---|
| Student | `Qwen/Qwen3-8B-Base` revision `49e3418fbbbca6ecbdf9608b4d22e5a407081db4` |
| Teacher | `Qwen/Qwen3-30B-A3B-Thinking-2507` traces (V4 paired think payload) |
| Problems | **4715** unique problems (`source_1ep_rows`); physical 2-epoch concat = **9430** rows |
| Dose | **32,436,894** assistant tokens / epoch; endpoint **64,873,788** assistant tokens (2 epochs) |
| Train seed | **42** |
## Recipe
- LoRA **r64 / α128**, dropout 0.0, seven projections: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
- Embeddings / `lm_head` frozen except **two-sided trainable B-rows** for **151643** (`<|endoftext|>`), **151667** (`<think>`), **151668** (`</think>`). Token ids from `adapter/TOKEN_ROWS_META.json`.
- Assistant supervised tail: `<|endoftext|>` (**151643**)
- LR **1e-4**, AdamW (β 0.9/0.95), **cosine** over assistant-token dose, **warmup 6%** (3,892,427 / 64,873,788 tokens), weight decay **0.1**
- **bf16**, no packing, no truncation, context 32768
- **2 epochs**, physical concat. Endpoint-only score; no checkpoint picking.
Merged weights are the 2-epoch endpoint (`step-000921`, 64,873,788 assistant tokens). LoRA + B-rows are under `adapter/`.
## Evaluation (DEV256)
256-problem LiveCodeBench-derived **dev** split. Seed **3407**, **think** mode, **no `<think>` prefill**, max generation ~32k, sandbox-verified **pass@1**. Temperature 0.6, top-p 0.95, top-k 20.
**Cap** = generations that hit the 32k length limit without closing `</think>`.
| Model | pass@1 | Cap | Notes |
|---|---:|---:|---|
| **code-think-q8b-20260908** | **81/256** | **130** | this repo; seed 3407; V4 think contract |
| Qwen3-8B-Base | 55/256 | — | **old-contract** historical bare-base number (not a same-contract V4 think-mode re-eval; a same-contract 8B-base think COMPLETE.json was not on disk) |
Single seed. These are research checkpoints, not product scores.
For context, the same-family 4B base on the **current** V4 think contract is 63/256 (seed 3407), with a 5-seed band 55.2 ± 4.9 on an earlier bare-base protocol.
## Usage
Merged full weights; no PEFT required at inference. Think mode: `enable_thinking=True`, **do not** prefill `<think>`.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "modrill/code-think-q8b-20260908"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
repo, torch_dtype="bfloat16", device_map="auto"
)
system = (
"You are an expert Python programmer. You will be given a question "
"(problem specification) and will generate a correct Python program that "
"matches the specification and passes all tests. You will NOT return "
"anything except for the program."
)
messages = [
{"role": "system", "content": system},
{"role": "user", "content": problem_statement},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(
**inputs,
max_new_tokens=32768,
do_sample=True,
temperature=0.6,
top_p=0.95,
top_k=20,
)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))
```
Stop ids used in the official eval: `151643` (`<|endoftext|>`), `151645` (`<|im_end|>`).
## Repo layout
- Root: merged HF weights plus `OFFICIAL_MERGE_RECEIPT.json`
- `adapter/`: LoRA, `token_rows_both_sides.safetensors`, `TOKEN_ROWS_META.json`, checkpoint `MANIFEST.json`
- `provenance/`: train `RUN_IDENTITY.json`, `TRAINING_CONFIG.json`, `POLICY.json`; DEV256 `COMPLETE.json`
- `MANIFEST.sha256`
Optimizer / resume states are **not** included.
|