metadata
language:
- en
license: apache-2.0
base_model: Locutusque/TinyMistral-248M
datasets:
- HuggingFaceH4/ultrachat_200k
- Felladrin/ChatML-ultrachat_200k
- Open-Orca/OpenOrca
- Felladrin/ChatML-OpenOrca
- hkust-nlp/deita-10k-v0
- Felladrin/ChatML-deita-10k-v0
- LDJnr/Capybara
- Felladrin/ChatML-Capybara
- databricks/databricks-dolly-15k
- Felladrin/ChatML-databricks-dolly-15k
- euclaise/reddit-instruct-curated
- Felladrin/ChatML-reddit-instruct-curated
- CohereForAI/aya_dataset
- Felladrin/ChatML-aya_dataset
pipeline_tag: text-generation
widget:
- messages:
- role: system
content: >-
You are a highly knowledgeable and friendly assistant. Your goal is to
understand and respond to user inquiries with clarity. Your
interactions are always respectful, helpful, and focused on delivering
the most accurate information to the user.
- role: user
content: Hey! Got a question for you!
- role: assistant
content: Sure! What's it?
- role: user
content: What are some potential applications for quantum computing?
- messages:
- role: user
content: Heya!
- role: assistant
content: Hi! How may I help you?
- role: user
content: >-
I'm interested in developing a career in software engineering. What
would you recommend me to do?
- messages:
- role: user
content: Morning!
- role: assistant
content: Good morning! How can I help you today?
- role: user
content: Could you give me some tips for becoming a healthier person?
- messages:
- role: system
content: >-
You are a very creative assistant. User will give you a task, which
you should complete with all your knowledge.
- role: user
content: >-
Hello! Can you please elaborate a background story of an RPG game
about wizards and dragons in a sci-fi world?
inference:
parameters:
max_new_tokens: 250
penalty_alpha: 0.5
top_k: 5
model-index:
- name: TinyMistral-248M-Chat-v2
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: 23.29
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
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: 27.39
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
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: 23.52
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
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: 41.32
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
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: 49.01
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
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
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/TinyMistral-248M-Chat-v2
name: Open LLM Leaderboard
Locutusque's TinyMistral-248M trained on chat datasets
- Base model: Locutusque/TinyMistral-248M with two additional special tokens (
<|im_start|>and<|im_end|>) - Datasets:
- License: Apache License 2.0
- Availability in other ML formats:
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: 5
Usage Example
from transformers import pipeline
generate = pipeline("text-generation", "Felladrin/TinyMistral-248M-Chat-v2")
messages = [
{
"role": "system",
"content": "You are a highly knowledgeable and friendly assistant. Your goal is to understand and respond to user inquiries with clarity. Your interactions are always respectful, helpful, and focused on delivering the most accurate information to the user.",
},
{
"role": "user",
"content": "Hey! Got a question for you!",
},
{
"role": "assistant",
"content": "Sure! What's it?",
},
{
"role": "user",
"content": "What are some potential applications for quantum computing?",
},
]
prompt = generate.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
output = generate(
prompt,
max_new_tokens=256,
penalty_alpha=0.5,
top_k=5,
)
print(output[0]["generated_text"])
How it was trained
This model was trained with SFTTrainer using the following settings:
| Hyperparameter | Value |
|---|---|
| Learning rate | 2e-5 |
| Total train batch size | 32 |
| Max. sequence length | 2048 |
| Weight decay | 0.01 |
| Warmup ratio | 0.1 |
| NEFTune Noise Alpha | 5 |
| Optimizer | Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| Scheduler | cosine |
| Seed | 42 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 27.42 |
| AI2 Reasoning Challenge (25-Shot) | 23.29 |
| HellaSwag (10-Shot) | 27.39 |
| MMLU (5-Shot) | 23.52 |
| TruthfulQA (0-shot) | 41.32 |
| Winogrande (5-shot) | 49.01 |
| GSM8k (5-shot) | 0.00 |