Felladrin_-_Pythia-31M-Chat-v1-gguf

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Pythia-31M-Chat-v1 - GGUF

• Model creator: https://huggingface.co/Felladrin/
• Original model: https://huggingface.co/Felladrin/Pythia-31M-Chat-v1/

Original model description:

language:

• en license: apache-2.0 base_model: EleutherAI/pythia-31m datasets:
• totally-not-an-llm/EverythingLM-data-V3
• databricks/databricks-dolly-15k
• THUDM/webglm-qa
• starfishmedical/webGPT_x_dolly
• Amod/mental_health_counseling_conversations
• sablo/oasst2_curated
• cognitivecomputations/wizard_vicuna_70k_unfiltered
• mlabonne/chatml_dpo_pairs pipeline_tag: text-generation widget:
  • messages:
    • role: system content: >- You are a career counselor. The user will provide you with an individual looking for guidance in their professional life, and your task is to assist them in determining what careers they are most suited for based on their skills, interests, and experience. You should also conduct research into the various options available, explain the job market trends in different industries, and advice on which qualifications would be beneficial for pursuing particular fields.
    • role: user content: Heya!
    • role: assistant content: Hi! How may I help you?
    • role: user content: >- I am interested in developing a career in software engineering. What would you recommend me to do?
  • messages:
    • role: system content: "You are a helpful assistant who answers user's questions with details and curiosity."
    • role: user content: What are some potential applications for quantum computing?
  • messages:
    • role: system content: You are a highly knowledgeable assistant. Help the user as much as you can.
    • role: user content: What are some steps I can take to become a healthier person? inference: parameters: max_new_tokens: 250 penalty_alpha: 0.5 top_k: 2 repetition_penalty: 1.0016 model-index:
• name: Pythia-31M-Chat-v1 results:
  • task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
    • type: acc_norm value: 22.7 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
    • type: acc_norm value: 25.6 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
    • type: acc value: 23.24 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
    • type: acc value: 47.99 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
    • type: mc2 value: 0.0 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
    • type: acc value: 0.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1 name: Open LLM Leaderboard

---

A Pythia Chat Model of 31M Parameters

• Base model: EleutherAI/pythia-31m
• Availability in other ML formats:
  • GGUF: Felladrin/gguf-Pythia-31M-Chat-v1
  • ONNX: Felladrin/onnx-Pythia-31M-Chat-v1

Recommended prompt format

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant

Recommended inference parameters

penalty_alpha: 0.5
top_k: 2
repetition_penalty: 1.0016

Datasets and parameters used for training

SFTTrainer(
    model,
    train_dataset=train_dataset,
    dataset_text_field="text",
    eval_dataset=eval_dataset,
    max_seq_length=2048,
    packing=True,
    args=TrainingArguments(
        learning_rate=2e-6,
        per_device_train_batch_size=1,
        per_device_eval_batch_size=1,
        gradient_accumulation_steps=16,
        lr_scheduler_type="cosine",
        num_train_epochs=1,
        logging_strategy="steps",
        save_strategy="steps",
        evaluation_strategy="steps",
        logging_steps=10,
        eval_steps=10,
        save_steps=10,
        warmup_steps=50,
        load_best_model_at_end=True,
        metric_for_best_model="eval_loss",
        greater_is_better=False,
        weight_decay=0.01,
        save_total_limit=10,
        neftune_noise_alpha=5,
    ),
    callbacks=[
        EarlyStoppingCallback(
            early_stopping_patience=3,
            early_stopping_threshold=0.005
        ),
    ],
)

DPOTrainer(
    model,
    beta=0.1,
    train_dataset=dataset,
    tokenizer=tokenizer,
    eval_dataset=eval_dataset,
    max_length=1536,
    max_prompt_length=1024,
    args=TrainingArguments(
        learning_rate=2e-6,
        per_device_train_batch_size=1,
        per_device_eval_batch_size=1,
        gradient_accumulation_steps=1,
        lr_scheduler_type="cosine",
        num_train_epochs=1,
        logging_strategy="steps",
        save_strategy="steps",
        evaluation_strategy="steps",
        logging_steps=1,
        eval_steps=1,
        save_steps=1,
        warmup_steps=0,
        load_best_model_at_end=True,
        metric_for_best_model="eval_loss",
        greater_is_better=False,
        weight_decay=0.0,
        neftune_noise_alpha=5,
        remove_unused_columns=False,
    ),
    callbacks=[
        EarlyStoppingCallback(
            early_stopping_patience=3,
            early_stopping_threshold=0.005
        ),
    ],
)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here