ArturBaranowskiAA
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Parent(s):
23e67c2
Update README.md and config.
Browse files- README.md +19 -6
- config.json +1 -1
- modeling_pharia.py +0 -1
README.md
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@@ -12,17 +12,30 @@ We provide a joint model card for `Pharia-1-LLM-7B-control` and `Pharia-1-LLM-co
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# Usage
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```python
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from transformers import AutoModelForCausalLM, PreTrainedTokenizerFast
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tokenizer = PreTrainedTokenizerFast.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(MODEL_ID, trust_remote_code=True)
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print(generated_text)
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```
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# Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, PreTrainedTokenizerFast
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INPUT = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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You are a helpful assistant. You give engaging, well-structured answers to user inquiries.<|eot_id|><|start_header_id|>user<|end_header_id|>
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When was Rome founded?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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"""
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MODEL_ID = "Aleph-Alpha/Pharia-1-LLM-7B-control-hf"
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tokenizer = PreTrainedTokenizerFast.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(MODEL_ID, trust_remote_code=True, torch_dtype=torch.bfloat16)
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model = model.to(device)
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inputs = tokenizer(INPUT, return_token_type_ids=False, return_tensors="pt").to(device)
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outputs = model.generate(**inputs, max_new_tokens=50)
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generated_text = tokenizer.decode(outputs[0])
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print(generated_text)
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```
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config.json
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@@ -24,7 +24,7 @@
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"rope_scaling": null,
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"torch_dtype": "
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"transformers_version": "4.44.2",
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"use_cache": true,
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"vocab_size": 128000
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"rope_scaling": null,
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.2",
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"use_cache": true,
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"vocab_size": 128000
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modeling_pharia.py
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@@ -764,7 +764,6 @@ class PhariaForCausalLM(PhariaPreTrainedModel):
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hidden_states = outputs[0]
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logits = self.lm_head(hidden_states)
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logits = logits.float()
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return CausalLMOutputWithPast(
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loss=0.0,
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hidden_states = outputs[0]
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logits = self.lm_head(hidden_states)
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return CausalLMOutputWithPast(
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loss=0.0,
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