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

yml

penalty_alpha: 0.5
top_k: 2
repetition_penalty: 1.0016

Datasets and parameters used for training

Dataset	License Type
totally-not-an-llm/EverythingLM-data-V3	mit
databricks/databricks-dolly-15k	cc-by-sa-3.0
THUDM/webglm-qa	apache-2.0
starfishmedical/webGPT_x_dolly	cc-by-sa-3.0
Amod/mental_health_counseling_conversations	openrail
sablo/oasst2_curated	apache-2.0
cognitivecomputations/wizard_vicuna_70k_unfiltered	apache-2.0
mlabonne/chatml_dpo_pairs	apache-2.0

python

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
        ),
    ],
)

python

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

Metric	Value
Avg.	19.92
AI2 Reasoning Challenge (25-Shot)	22.70
HellaSwag (10-Shot)	25.60
MMLU (5-Shot)	23.24
TruthfulQA (0-shot)	0.00
Winogrande (5-shot)	47.99
GSM8k (5-shot)	0.00

RichardErkhov/Felladrin_-_Pythia-31M-Chat-v1-8bits

Original model description:

A Pythia Chat Model of 31M Parameters

Recommended prompt format

Recommended inference parameters

Datasets and parameters used for training

Open LLM Leaderboard Evaluation Results

RichardErkhov/
Felladrin_-_Pythia-31M-Chat-v1-8bits