初始化项目,由ModelHub XC社区提供模型
Model: migtissera/Llama-3-8B-Synthia-v3.5 Source: Original Platform
This commit is contained in:
114
README.md
Normal file
114
README.md
Normal file
@@ -0,0 +1,114 @@
|
||||
---
|
||||
license: llama3
|
||||
---
|
||||
|
||||
|
||||
# Llama-3-8B-Synthia-v3.5
|
||||
Llama-3-8B-Synthia-v3.5 (Synthetic Intelligent Agent) is a general purpose Large Language Model (LLM). It was trained on the Synthia-v3.5 dataset that contains the varied system contexts, plus some other publicly available datasets.
|
||||
|
||||
It has been fine-tuned for instruction following as well as having long-form conversations.
|
||||
|
||||
Compute for Llama-3-8B-Synthia-v3.5 was sponsored by [KindoAI](https://kindo.ai/).
|
||||
|
||||
<br>
|
||||
|
||||

|
||||
|
||||
<br>
|
||||
|
||||
|
||||
|
||||
|
||||
## Evaluation
|
||||
|
||||
We evaluated Llama-3-8B-Synthia-v3.5 on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.
|
||||
|
||||
Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). Section to follow.
|
||||
|
||||
||||
|
||||
|:------:|:--------:|:-------:|
|
||||
|**Task**|**Metric**|**Value**|
|
||||
|*arc_challenge*|acc_norm||
|
||||
|*hellaswag*|acc_norm||
|
||||
|*mmlu*|acc_norm||
|
||||
|*truthfulqa_mc*|mc2||
|
||||
|**Total Average**|-|||
|
||||
|
||||
<br>
|
||||
|
||||
|
||||
# Sample code to run inference
|
||||
|
||||
```python
|
||||
import torch, json
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
model_path = "/home/migel/Tess-2.0-Llama-3-8B"
|
||||
output_file_path = "/home/migel/conversations.jsonl"
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_path,
|
||||
torch_dtype=torch.float16,
|
||||
device_map="auto",
|
||||
load_in_4bit=False,
|
||||
trust_remote_code=False,
|
||||
)
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
|
||||
def generate_text(instruction):
|
||||
tokens = tokenizer.encode(instruction)
|
||||
tokens = torch.LongTensor(tokens).unsqueeze(0)
|
||||
tokens = tokens.to("cuda")
|
||||
|
||||
instance = {
|
||||
"input_ids": tokens,
|
||||
"top_p": 1.0,
|
||||
"temperature": 0.75,
|
||||
"generate_len": 1024,
|
||||
"top_k": 50,
|
||||
}
|
||||
|
||||
length = len(tokens[0])
|
||||
with torch.no_grad():
|
||||
rest = model.generate(
|
||||
input_ids=tokens,
|
||||
max_length=length + instance["generate_len"],
|
||||
use_cache=True,
|
||||
do_sample=True,
|
||||
top_p=instance["top_p"],
|
||||
temperature=instance["temperature"],
|
||||
top_k=instance["top_k"],
|
||||
num_return_sequences=1,
|
||||
pad_token_id=tokenizer.eos_token_id,
|
||||
)
|
||||
output = rest[0][length:]
|
||||
string = tokenizer.decode(output, skip_special_tokens=True)
|
||||
return f"{string}"
|
||||
|
||||
conversation = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are Synthia, a helful, female AI assitant. You always provide detailed answers without hesitation.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"""
|
||||
|
||||
while True:
|
||||
user_input = input("You: ")
|
||||
llm_prompt = f"{conversation}{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
|
||||
answer = generate_text(llm_prompt)
|
||||
print(answer)
|
||||
|
||||
conversation = f"{llm_prompt}{answer}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
|
||||
|
||||
json_data = {"prompt": user_input, "answer": answer}
|
||||
|
||||
with open(output_file_path, "a") as output_file:
|
||||
output_file.write(json.dumps(json_data) + "\n")
|
||||
```
|
||||
|
||||
# Join My General AI Discord (NeuroLattice):
|
||||
https://discord.gg/Hz6GrwGFKD
|
||||
|
||||
# Limitations & Biases:
|
||||
|
||||
While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
|
||||
|
||||
Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
|
||||
|
||||
Exercise caution and cross-check information when necessary. This is an uncensored model.
|
||||
Reference in New Issue
Block a user