language, license, tags, datasets, inference, model-index, pipeline_tag

language	license	tags	datasets	inference	model-index	pipeline_tag
en	apache-2.0	text-generation-inference	HuggingFaceH4/ultrachat_200k openchat/openchat_sharegpt4_dataset Open-Orca/SlimOrca	false	name results falcon-rw-1b-chat task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 35.58 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-chat Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 61.12 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-chat Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 24.51 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-chat Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 39.62 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-chat Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 61.72 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-chat Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 1.67 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-chat Open LLM Leaderboard	text-generation

🌟 Falcon-RW-1B-Chat

Falcon-RW-1B-Chat is a conversational model with 1 billion parameters. It's a further refinement of the Falcon-RW-1B-Instruct-OpenOrca, trained on selected data from the HuggingFaceH4/ultrachat_200k and openchat/openchat_sharegpt4_dataset datasets.

✨Try it out at our Tiny Chat space running on free-tier hardware!✨

The underlying Falcon-RW-1B-Instruct-OpenOrca model is built on the Falcon-RW-1B, a causal decoder-only model. It has been instruction-finetuned using the Open-Orca/SlimOrca dataset.

🎯 Purpose

The Falcon-RW-1B-Chat aims to add conversational capabilities to the Falcon-RW-1B-Instruct-OpenOrca model. This initiative is driven by the need for a smaller, open-source, instruction-finetuned, ready-to-use model, suitable for users with limited computational resources, like lower-end consumer GPUs.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	37.37
AI2 Reasoning Challenge (25-Shot)	35.58
HellaSwag (10-Shot)	61.12
MMLU (5-Shot)	24.51
TruthfulQA (0-shot)	39.62
Winogrande (5-shot)	61.72
GSM8k (5-shot)	1.67

📖 Example Code

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "ericzzz/falcon-rw-1b-chat"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, device_map="auto", torch_dtype=torch.bfloat16
)

chat_history = [
    {"role": "user", "content": "Hello!"},
    {"role": "assistant", "content": "Hello! How can I assist you today?"},
    {"role": "user", "content": "Explain what AI is."},
]

input_ids = tokenizer.apply_chat_template(
    chat_history, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
output_tokens = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.7,
    repetition_penalty=1.05,
    max_new_tokens=200,
)
output_text = tokenizer.decode(
    output_tokens[0][len(input_ids[0]) :], skip_special_tokens=True
)

print(output_text)

⚠️ Limitations

This model may generate inaccurate or misleading information and is prone to hallucination, creating plausible but false narratives. It lacks the ability to discern factual content from fiction and may inadvertently produce biased, harmful or offensive content. Its understanding of complex, nuanced queries is limited. Users should be aware of this and verify any information obtained from the model.

The model is provided 'as is' without any warranties, and the creators are not liable for any damages arising from its use. Users are responsible for their interactions with the model.

📬 Contact

For further inquiries or feedback, please contact at eric.fu96@aol.com.