language:

• en
• fr
• es
• ru
• zh
• ja
• fa
• code license: mit library_name: transformers tags:
• fluently-lm
• fluently
• prinum
• instruct
• trained
• math
• roleplay
• reasoning
• axolotl
• unsloth
• argilla
• qwen2 datasets:
• fluently-sets/ultraset
• fluently-sets/ultrathink
• fluently-sets/reasoning-1-1k
• fluently-sets/MATH-500-Overall inference: true pipeline_tag: text-generation model-index:
• name: FluentlyLM-Prinum 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: 80.9 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum 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: 59.48 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum 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: 54.0 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum 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: 18.23 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum 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: 17.26 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum 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: 53.42 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fluently-lm/FluentlyLM-Prinum name: Open LLM Leaderboard

FluentlyLM Prinum (32B-version)

Introducing the first standalone model from Project Fluently LM! We worked on it for several months, used different approaches, and eventually found the optimal one.

Model Details

Model Description

• Developed by: @fluently-lm
• Model type: Causal Language Models (QwenForCausalLM, LM Transformer)
• Number of Parameters: 32.5B
• Number of Paramaters (Non-Embedding): 31.0B
• Number of Layers: 64
• Number of Attention Heads (GQA): 40 for Q and 8 for KV
• Context Length: Full 131,072 tokens
• Language(s) (NLP): English, French, Spanish, Russian, Chinese, Japanese, Persian (official support)
• License: MIT

Quickstart

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "fluently-lm/FluentlyLM-Prinum"

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

prompt = "Write a quick sort algorithm."
messages = [
    {"role": "system", "content": "You are FluentlyLM, created by Project Fluently. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=1024
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

GGUF-using

You can also use our model locally via GGUF file in various interfaces and workflows, we offer several repos for downloading GGUF:

• mradermacher/FluentlyLM-Prinum-GGUF (all GGUF-quants)
• fluently-lm/FluentlyLM-Prinum-Q4_K_M-GGUF (only Q4_K_M-quant) (coming soon...)

Model recipe

Evolution

🏆 12th place on Open LLM Leaderboard (21.02.2025)

Special thanks

🤗 We are grateful for open source resources, technologies and assistance from: Unsloth AI, Axolotl AI, Argilla, Alibaba Cloud: Qwen, NVIDIA and NousResearch.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here