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Uses
Direct Use
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import transformers
3import torch
4
5model = "Dhruvil47/falcon-7b-bioarxiv"
6
7tokenizer = AutoTokenizer.from_pretrained(model)
8pipeline = transformers.pipeline(
9 "text-generation",
10 model=model,
11 tokenizer=tokenizer,
12 torch_dtype=torch.bfloat16,
13 trust_remote_code=True,
14 device_map="auto",
15)
16sequences = pipeline(
17 "NKX2-1 works through",
18 max_length=200,
19 do_sample=True,
20 top_k=10,
21 num_return_sequences=1,
22 eos_token_id=tokenizer.eos_token_id,
23)
24for seq in sequences:
25 print(f"Result: {seq['generated_text']}")
26
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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
Testing Data
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Results
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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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Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
Framework versions
