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RichardErkhov/
piotr25691_-_thea-3b-25r-gguf

Name: piotr25691_-_thea-3b-25r-gguf
Availability: InStock

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thea-3b-25r - GGUF

Model creator: https://huggingface.co/piotr25691/
Original model: https://huggingface.co/piotr25691/thea-3b-25r/

Name	Quant method	Size
thea-3b-25r.Q2_K.gguf	Q2_K	1.27GB
thea-3b-25r.IQ3_XS.gguf	IQ3_XS	1.38GB
thea-3b-25r.IQ3_S.gguf	IQ3_S	1.44GB
thea-3b-25r.Q3_K_S.gguf	Q3_K_S	1.44GB
thea-3b-25r.IQ3_M.gguf	IQ3_M	1.49GB
thea-3b-25r.Q3_K.gguf	Q3_K	1.57GB
thea-3b-25r.Q3_K_M.gguf	Q3_K_M	1.57GB
thea-3b-25r.Q3_K_L.gguf	Q3_K_L	1.69GB
thea-3b-25r.IQ4_XS.gguf	IQ4_XS	1.71GB
thea-3b-25r.Q4_0.gguf	Q4_0	1.79GB
thea-3b-25r.IQ4_NL.gguf	IQ4_NL	1.79GB
thea-3b-25r.Q4_K_S.gguf	Q4_K_S	1.8GB
thea-3b-25r.Q4_K.gguf	Q4_K	1.88GB
thea-3b-25r.Q4_K_M.gguf	Q4_K_M	1.88GB
thea-3b-25r.Q4_1.gguf	Q4_1	1.95GB
thea-3b-25r.Q5_0.gguf	Q5_0	2.11GB
thea-3b-25r.Q5_K_S.gguf	Q5_K_S	2.11GB
thea-3b-25r.Q5_K.gguf	Q5_K	2.16GB
thea-3b-25r.Q5_K_M.gguf	Q5_K_M	2.16GB
thea-3b-25r.Q5_1.gguf	Q5_1	2.28GB
thea-3b-25r.Q6_K.gguf	Q6_K	2.46GB
thea-3b-25r.Q8_0.gguf	Q8_0	3.19GB

Original model description:

language:

en license: llama3.2 tags:
text-generation-inference
transformers
llama
trl
sft
reasoning
llama-3 base_model: chuanli11/Llama-3.2-3B-Instruct-uncensored datasets:
KingNish/reasoning-base-20k
piotr25691/thea-name-overrides model-index:
name: thea-3b-25r results:
- task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics:
  - type: inst_level_strict_acc and prompt_level_strict_acc value: 73.44 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=piotr25691/thea-3b-25r name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics:
  - type: acc_norm value: 22.55 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=piotr25691/thea-3b-25r name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics:
  - type: exact_match value: 16.31 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=piotr25691/thea-3b-25r name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics:
  - type: acc_norm value: 2.35 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=piotr25691/thea-3b-25r name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics:
  - type: acc_norm value: 3.57 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=piotr25691/thea-3b-25r name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 24.25 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=piotr25691/thea-3b-25r name: Open LLM Leaderboard

Model Description

An uncensored reasoning Llama 3.2 3B model trained on reasoning data.

It has been trained using improved training code, and gives an improved performance. Here is what inference code you should use:

from transformers import AutoModelForCausalLM, AutoTokenizer

MAX_REASONING_TOKENS = 1024
MAX_RESPONSE_TOKENS = 512

model_name = "piotr25691/thea-3b-25r"

model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Which is greater 9.9 or 9.11 ??"
messages = [
    {"role": "user", "content": prompt}
]

# Generate reasoning
reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)

print("REASONING: " + reasoning_output)

# Generate answer
messages.append({"role": "reasoning", "content": reasoning_output})
response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)

print("ANSWER: " + response_output)

Trained by: Piotr Zalewski
License: llama3.2
Finetuned from model: chuanli11/Llama-3.2-3B-Instruct-uncensored
Dataset used: KingNish/reasoning-base-20k

This Llama model was trained faster than Unsloth using custom training code.

Visit https://www.kaggle.com/code/piotr25691/distributed-llama-training-with-2xt4 to find out how you can finetune your models using BOTH of the Kaggle provided GPUs.

Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more quants, at much higher speed, than I would otherwise be able to.

RichardErkhov/piotr25691_-_thea-3b-25r-gguf

Original model description:

Model Description

RichardErkhov/
piotr25691_-_thea-3b-25r-gguf