Model Card for Model ID
Llama3 - Job Coaching Model
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by: Doyoon Jung [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
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- Finetuned from model [optional]: [More Information Needed]
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- Repository: [More Information Needed]
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Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Do-Yoon Jung (rabbitsun2@gmail.com)
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: Apache License
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: meta-llama/Meta-Llama-3-8B-Instruct
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original llama3 codebase.
Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the generate() function. Let's see examples of both.
Transformers pipeline
1import transformers
2import torch
3
4model_id = "dodo2/llama3-coaching-ko-8b-dodo"
5
6pipeline = transformers.pipeline(
7 "text-generation",
8 model=model_id,
9 model_kwargs={"torch_dtype": torch.bfloat16},
10 device_map="auto",
11)
12
13messages = [
14 {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
15 {"role": "user", "content": "Who are you?"},
16]
17
18terminators = [
19 pipeline.tokenizer.eos_token_id,
20 pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
21]
22
23outputs = pipeline(
24 messages,
25 max_new_tokens=256,
26 eos_token_id=terminators,
27 do_sample=True,
28 temperature=0.6,
29 top_p=0.9,
30)
31print(outputs[0]["generated_text"][-1])
Transformers AutoModelForCausalLM
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4model_id = "dodo2/llama3-coaching-ko-8b-dodo"
5
6tokenizer = AutoTokenizer.from_pretrained(model_id)
7model = AutoModelForCausalLM.from_pretrained(
8 model_id,
9 torch_dtype=torch.bfloat16,
10 device_map="auto",
11)
12
13messages = [
14 {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
15 {"role": "user", "content": "Who are you?"},
16]
17
18input_ids = tokenizer.apply_chat_template(
19 messages,
20 add_generation_prompt=True,
21 return_tensors="pt"
22).to(model.device)
23
24terminators = [
25 tokenizer.eos_token_id,
26 tokenizer.convert_tokens_to_ids("<|eot_id|>")
27]
28
29outputs = model.generate(
30 input_ids,
31 max_new_tokens=256,
32 eos_token_id=terminators,
33 do_sample=True,
34 temperature=0.6,
35 top_p=0.9,
36)
37response = outputs[0][input_ids.shape[-1]:]
38print(tokenizer.decode(response, skip_special_tokens=True))
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
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Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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