Foxglove is a well-rounded RP model. It is smart, does a great job of sticking to character card, and is proficient at following desired markdown.
Foxglove_7B is a merge of the following models using
LazyMergekit:
Thanks to mradermacher, static GGUF quants are available
here.
Alpaca works best, but Mistral provides good outputs as well.
1slices:
2 - sources:
3 - model: ResplendentAI/Datura_7B
4 layer_range: [0, 32]
5 - model: Epiculous/Mika-7B
6 layer_range: [0, 32]
7merge_method: slerp
8base_model: ResplendentAI/Datura_7B
9parameters:
10 t:
11 - filter: self_attn
12 value: [0, 0.7, 0.4, 0.6, 1]
13 - filter: mlp
14 value: [0.8, 0.5, 0.7, 0.3, 0]
15 - value: 0.6
16dtype: bfloat16
1!pip install -qU transformers accelerate
2
3from transformers import AutoTokenizer
4import transformers
5import torch
6
7model = "rmdhirr/Foxglove_7B"
8messages = [{"role": "user", "content": "What is a large language model?"}]
9
10tokenizer = AutoTokenizer.from_pretrained(model)
11prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
12pipeline = transformers.pipeline(
13 "text-generation",
14 model=model,
15 torch_dtype=torch.float16,
16 device_map="auto",
17)
18
19outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
20print(outputs[0]["generated_text"])
Detailed results can be found
here
