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Model Details
Model Provider
axonlabsai
Model Tree
huihui-ai/DeepSeek-R1-Distill-Qwen-7B-abliterated-v2
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoTokenizer, AutoModelForCausalLMm = "axonlabsai/Ranger-7B"tok = AutoTokenizer.from_pretrained(m)model = AutoModelForCausalLM.from_pretrained(m, device_map="auto", dtype="auto")msgs = [{"role": "user", "content": "Write a Python LRU cache with O(1) get and put."}]text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)out = model.generate(**tok(text, return_tensors="pt").to(model.device), max_new_tokens=1024)print(tok.decode(out[0], skip_special_tokens=True))
Tool calling is supported through the standard tools= argument:
python
tools = [{"type": "function", "function": {"name": "run_tests","description": "Run the project's test suite.","parameters": {"type": "object", "properties": {"path": {"type": "string"}}},}}]text = tok.apply_chat_template(msgs, tools=tools, tokenize=False, add_generation_prompt=True)
Quantised builds live in gguf/ for llama.cpp, Ollama,
LM Studio, and anything else in that ecosystem.
| file | bits | size | use it when |
|---|---|---|---|
Ranger-7B-Q4_K_M.gguf | 4 | ~4.7 GB | default — fits 8GB VRAM or plain CPU |
Ranger-7B-Q5_K_M.gguf | 5 | ~5.4 GB | a little more headroom, better fidelity |
Ranger-7B-Q8_0.gguf | 8 | ~8.1 GB | near-lossless, 12GB+ VRAM |
Ranger-7B-F16.gguf | 16 | ~15.2 GB | reference / for making your own quants |
bash
llama-cli -hf axonlabsai/Ranger-7B:Q4_K_M -p "Write a Python LRU cache."# or locallyllama-cli -m Ranger-7B-Q4_K_M.gguf -c 8192 -p "..."
Because the model is terse, a 1024-token budget is genuinely enough for most work — which is the entire point of it.
A LoRA (rank 64, alpha 128) over all attention and MLP projections across all 28 layers, merged into the weights — the model ships adapter-free. It trains model identity and response discipline.
The chat template was also rebuilt, fixing two real defects in the stock one:
tools list and renders it, so the model can be told which
functions exist. The stock template could emit tool calls but had no way to
declare tools, and raised a type error when handed any.<think> content on the final turn while still stripping stale
reasoning from earlier turns. The stock template stripped it from every
assistant message.No standard suite has been run end to end on this checkpoint, and no leaderboard scores are claimed for it. Everything below was measured directly, and the numbers that are noisy are labelled as noisy.
HumanEval+ (32 problems, executed). 65.6% at a 1000-token budget vs the base's 34.4%. At n=32 the standard error on a single proportion is ±8.4 points, so treat the accuracy difference as suggestive rather than proven — a paired McNemar test gives p between 0.25 and 0.58. What is not noisy: the base emitted no code at all on 10 of 32 problems because it was still reasoning when the budget ran out, 27 of 32 of its responses hit the ceiling, and the 6x output-length difference holds across every problem. Given 3000 tokens the base recovers to 56.2% — still under this model at a third of the budget. In fairness to the base, that 56.2% comes from re-running only the problems where it produced nothing, so it is probably a slight underestimate.
Math is unchanged. On 4 competition problems (AIME 2024, MATH-500-hard, a counting problem) both this model and the base score 2/4, passing and failing the same problems. It reaches AIME 2024 Problem 1 (answer 204) in 2592 characters where the base takes 6699. Four problems is a regression check, not a score.
Identity: 8/12 held-out probes.
A separate pass injected ~2,400 examples of coding data — OpenCodeReasoning traces, execution-verified OpenCodeInstruct solutions, CodeFeedback revision pairs. It moved HumanEval+ pass@1 by exactly zero (21/32 before, 21/32 after; five problems gained, five lost). A few thousand examples at LoRA rank 64 shifts style, not competence. That checkpoint was discarded rather than shipped as an upgrade, and the finding is published here because it is more useful than another rounded-up number.
Built on
huihui-ai/DeepSeek-R1-Distill-Qwen-7B-abliterated-v2,
an abliterated release of DeepSeek-R1-Distill-Qwen-7B, itself distilled from
Qwen2.5-Math-7B on 800k DeepSeek-R1 samples. That maths-heavy lineage is why the
base arrives strong at reasoning and comparatively untrained at code.
For reference, the published figures for the underlying R1-Distill-Qwen-7B — measured by others, on the base, not claims about this checkpoint:
| benchmark | score |
|---|---|
| MATH-500 (pass@1) | 92.8 |
| AIME 2024 (cons@64) | 83.3 |
| AIME 2024 (pass@1) | 55.5 |
| GPQA-Diamond (pass@1) | 49.1 |
| LiveCodeBench (pass@1) | 37.6 |
| CodeForces (rating) | 1189 |
Note the honest shape of those: elite at maths, mid at competitive programming and graduate-level science. Anyone quoting a LiveCodeBench figure in the 80s for a 7B is quoting a much larger model.
MIT, inherited from DeepSeek-R1-Distill-Qwen-7B. The Qwen2.5 lineage beneath it is Apache-2.0.