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Claude OSS 9b

Name: claude-oss
Availability: InStock

Disclaimer: This is not an official release by Anthropic.
Claude OSS 9B is an independent open model project.

Overview

Claude OSS 9B is a multilingual conversational language model designed to deliver a familiar polished assistant experience with strong instruction-following, stable identity behavior, and practical general-purpose usefulness.

The model was fine-tuned on open-source datasets, with a combined total of approximately 200,000 rows collected from Hugging Face. The training mixture focused on assistant behavior, reasoning preservation, multilingual interaction, and stronger identity consistency.

Claude OSS 9B is intended for:

general chat and assistant use
multilingual interaction
reasoning-oriented prompting
writing and summarization
lightweight coding help
identity-consistent assistant behavior
200+ languages

Benchmarks

(Based on Qwen3.5 9b benchmarks results)

Training Summary

Claude OSS 9B was fine-tuned on a curated open-source training mixture totaling roughly 200k rows from Hugging Face. The data mix emphasized:

assistant-style conversations
instruction following
identity reinforcement
multilingual prompts and answers
reasoning preservation
general usability tasks

Usage

Transformers

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "squ11z1/claude-oss-9b"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    device_map="auto",
)

messages = [{"role": "user", "content": "Who are you?"},]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
)

inputs = {k: v.to(model.device) for k, v in inputs.items()}

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=False,
        pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
    )

prompt_len = inputs["input_ids"].shape[1]
print(tokenizer.decode(outputs[0][prompt_len:], skip_special_tokens=True))

GGUF / llama.cpp

bash

  ./llama-cli -m claude-oss-9b-q4_k_m.gguf -p "Who are you?"

ansulev/claude-oss

Claude OSS 9b

Overview

Benchmarks

Training Summary

Usage

ansulev/
claude-oss