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README.md

@@ -1,13 +1,5 @@
 ---
 library_name: peft
-base_model: TheBloke/zephyr-7B-beta-GPTQ
-revision: gptq-8bit-32g-actorder_True
-license: mit
-language:
-- pt
-tags:
-- gptq
-- ptbr
 ---
 ## Training procedure
 
@@ -32,75 +24,5 @@ The following `bitsandbytes` quantization config was used during training:
 - max_input_length: None
 ### Framework versions
 
-# Load model
-```python
-from transformers import AutoModelForCausalLM, GPTQConfig
-from peft import PeftModel
 
-bnb_config = GPTQConfig(
-    bits=8,
-    disable_exllama=True,
-)
-
-_model = AutoModelForCausalLM.from_pretrained(
-    'TheBloke/zephyr-7B-beta-GPTQ',
-    quantization_config=bnb_config,
-    device_map='auto',
-    revision='gptq-8bit-32g-actorder_True',
-)
-
-model = PeftModel.from_pretrained(_model, 'matheusrdgsf/cesar-ptbr')
-```
-
-# Easy inference
-```python
-from transformers import GenerationConfig
-from transformers import AutoTokenizer
-
-tokenizer_model = AutoTokenizer.from_pretrained('TheBloke/zephyr-7B-beta-GPTQ')
-tokenizer_template = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-alpha')
-
-generation_config = GenerationConfig(
-    do_sample=True,
-    temperature=0.1,
-    top_p=0.25,
-    top_k=0,
-    max_new_tokens=512,
-    repetition_penalty=1.1,
-    eos_token_id=tokenizer_model.eos_token_id,
-    pad_token_id=tokenizer_model.eos_token_id,
-)
-
-
-def get_inference(
-    text,
-    model,
-    tokenizer_model=tokenizer_model,
-    tokenizer_template=tokenizer_template,
-    generation_config=generation_config,
-):
-    st_time = time.time()
-    inputs = tokenizer_model(
-        tokenizer_template.apply_chat_template(
-            [
-                {
-                    "role": "system",
-                    "content": "Você é um chatbot para indicação de filmes. Responda de maneira educada sugestões de filmes para os usuários.",
-                },
-                {"role": "user", "content": text},
-            ],
-            tokenize=False,
-        ),
-        return_tensors="pt",
-    ).to("cuda")
-
-    outputs = model.generate(**inputs, generation_config=generation_config)
-
-    print('inference time:', time.time() - st_time)
-    return tokenizer_model.decode(outputs[0], skip_special_tokens=True).split('\n')[-1]
-
-get_inference('Poderia indicar filmes de ação de até 2 horas?', model)
-```
-
-
-- PEFT 0.5.0
+- PEFT 0.5.0

adapter_config.json

@@ -1,7 +1,6 @@
 {
   "auto_mapping": null,
   "base_model_name_or_path": "TheBloke/zephyr-7B-beta-GPTQ",
-  "revision": "gptq-8bit-32g-actorder_True",
   "bias": "none",
   "fan_in_fan_out": false,
   "inference_mode": true,
@@ -13,6 +12,7 @@
   "modules_to_save": null,
   "peft_type": "LORA",
   "r": 16,
+  "revision": null,
   "target_modules": [
     "q_proj",
     "v_proj"

adapter_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:eb45b0e39fc1e82f20fb661a914f5951451557ee03efd76add36aff361041aad
+oid sha256:7435d98205bd733689ce9bf2192cc23b005f25ac6404b21b055022513d4092b5
 size 27308941