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| Name | Quant method | Size |
|---|---|---|
| TinyLlama-1.1B-Chat-v1.0.Q2_K.gguf | Q2_K | 0.4GB |
| TinyLlama-1.1B-Chat-v1.0.IQ3_XS.gguf | IQ3_XS | 0.44GB |
| TinyLlama-1.1B-Chat-v1.0.IQ3_S.gguf | IQ3_S | 0.47GB |
| TinyLlama-1.1B-Chat-v1.0.Q3_K_S.gguf | Q3_K_S | 0.47GB |
| TinyLlama-1.1B-Chat-v1.0.IQ3_M.gguf | IQ3_M | 0.48GB |
| TinyLlama-1.1B-Chat-v1.0.Q3_K.gguf | Q3_K | 0.51GB |
| TinyLlama-1.1B-Chat-v1.0.Q3_K_M.gguf | Q3_K_M | 0.51GB |
| TinyLlama-1.1B-Chat-v1.0.Q3_K_L.gguf | Q3_K_L | 0.55GB |
| TinyLlama-1.1B-Chat-v1.0.IQ4_XS.gguf | IQ4_XS | 0.57GB |
| TinyLlama-1.1B-Chat-v1.0.Q4_0.gguf | Q4_0 | 0.59GB |
| TinyLlama-1.1B-Chat-v1.0.IQ4_NL.gguf | IQ4_NL | 0.6GB |
| TinyLlama-1.1B-Chat-v1.0.Q4_K_S.gguf | Q4_K_S | 0.6GB |
| TinyLlama-1.1B-Chat-v1.0.Q4_K.gguf | Q4_K | 0.62GB |
| TinyLlama-1.1B-Chat-v1.0.Q4_K_M.gguf | Q4_K_M | 0.62GB |
| TinyLlama-1.1B-Chat-v1.0.Q4_1.gguf | Q4_1 | 0.65GB |
| TinyLlama-1.1B-Chat-v1.0.Q5_0.gguf | Q5_0 | 0.71GB |
| TinyLlama-1.1B-Chat-v1.0.Q5_K_S.gguf | Q5_K_S | 0.71GB |
| TinyLlama-1.1B-Chat-v1.0.Q5_K.gguf | Q5_K | 0.73GB |
| TinyLlama-1.1B-Chat-v1.0.Q5_K_M.gguf | Q5_K_M | 0.73GB |
| TinyLlama-1.1B-Chat-v1.0.Q5_1.gguf | Q5_1 | 0.77GB |
| TinyLlama-1.1B-Chat-v1.0.Q6_K.gguf | Q6_K | 0.84GB |
UltraChat dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
We then further aligned the model with 🤗 TRL's DPOTrainer on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4."1# Install transformers from source - only needed for versions <= v4.34
2# pip install git+https://github.com/huggingface/transformers.git
3# pip install accelerate
4
5import torch
6from transformers import pipeline
7
8pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")
9
10# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
11messages = [
12 {
13 "role": "system",
14 "content": "You are a friendly chatbot who always responds in the style of a pirate",
15 },
16 {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
17]
18prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
19outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
20print(outputs[0]["generated_text"])
21# <|system|>
22# You are a friendly chatbot who always responds in the style of a pirate.</s>
23# <|user|>
24# How many helicopters can a human eat in one sitting?</s>
25# <|assistant|>
26# ...