Quantization made by Richard Erkhov.
Eclipse-13B-dpo - GGUF
- Model creator: https://huggingface.co/Xenon1/
- Original model: https://huggingface.co/Xenon1/Eclipse-13B-dpo/
| Name | Quant method | Size |
|---|---|---|
| Eclipse-13B-dpo.Q2_K.gguf | Q2_K | 4.43GB |
| Eclipse-13B-dpo.IQ3_XS.gguf | IQ3_XS | 3.08GB |
| Eclipse-13B-dpo.IQ3_S.gguf | IQ3_S | 5.22GB |
| Eclipse-13B-dpo.Q3_K_S.gguf | Q3_K_S | 5.2GB |
| Eclipse-13B-dpo.IQ3_M.gguf | IQ3_M | 5.35GB |
| Eclipse-13B-dpo.Q3_K.gguf | Q3_K | 5.78GB |
| Eclipse-13B-dpo.Q3_K_M.gguf | Q3_K_M | 5.78GB |
| Eclipse-13B-dpo.Q3_K_L.gguf | Q3_K_L | 6.27GB |
| Eclipse-13B-dpo.IQ4_XS.gguf | IQ4_XS | 6.5GB |
| Eclipse-13B-dpo.Q4_0.gguf | Q4_0 | 6.78GB |
| Eclipse-13B-dpo.IQ4_NL.gguf | IQ4_NL | 6.85GB |
| Eclipse-13B-dpo.Q4_K_S.gguf | Q4_K_S | 6.84GB |
| Eclipse-13B-dpo.Q4_K.gguf | Q4_K | 7.25GB |
| Eclipse-13B-dpo.Q4_K_M.gguf | Q4_K_M | 7.25GB |
| Eclipse-13B-dpo.Q4_1.gguf | Q4_1 | 7.52GB |
| Eclipse-13B-dpo.Q5_0.gguf | Q5_0 | 8.26GB |
| Eclipse-13B-dpo.Q5_K_S.gguf | Q5_K_S | 8.26GB |
| Eclipse-13B-dpo.Q5_K.gguf | Q5_K | 8.51GB |
| Eclipse-13B-dpo.Q5_K_M.gguf | Q5_K_M | 8.51GB |
| Eclipse-13B-dpo.Q5_1.gguf | Q5_1 | 9.01GB |
| Eclipse-13B-dpo.Q6_K.gguf | Q6_K | 9.84GB |
| Eclipse-13B-dpo.Q8_0.gguf | Q8_0 | 12.75GB |
Original model description:
language: - en license: apache-2.0 tags: - mistral - Eclipse-13B-dpo pipeline_tag: text-generation
Model Card for Eclipse-13B-dpo
Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.
Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
This format is available as a chat template via the apply_chat_template() method:
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Xenon1/Eclipse-13B-dpo")
tokenizer = AutoTokenizer.from_pretrained("Xenon1/Eclipse-13B-dpo")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer