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使用 OpenAI 创建和管理开放权重模型的微调作业 APIs
兼容 OpenAI 的微调作业 APIs 允许您创建、监控和管理微调作业。本页重点介绍如何使用它们 APIs 进行钢筋微调。有关完整的 API 详细信息，请参阅OpenAI微调文档。
创建微调作业
创建微调作业，开始根据给定数据集创建新模型的过程。如需完整的 API 详细信息，请参阅OpenAI创建微调任务文档。
示例
要使用 RFT 方法创建微调作业，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
# Create fine-tuning job with RFT method
job_response = client.fine_tuning.jobs.create(
model=MODEL_ID,
training_file=training_file_id,
# Suffix field is not supported so commenting for now.
# suffix="rft-example", # Optional: suffix for fine-tuned model name
extra_body={
"method": {
"type": "reinforcement",
"reinforcement": {
"grader": {
"type": "lambda",
"lambda": {
"function": "arn:aws:lambda:us-west-2:123456789012:function:my-reward-function" # Replace with your Lambda ARN
}
},
"hyperparameters": {
"n_epochs": 1, # Number of training epochs
"batch_size": 4, # Batch size
"learning_rate_multiplier": 1.0 # Learning rate multiplier
}
}
}
}
)
# Store job ID for next steps
job_id = job_response.id
print({job_id})
- HTTP request
-
向以下地址发出 POST 请求/v1/fine_tuning/jobs：
curl https://bedrock-mantle.us-west-2.api.aws/v1/fine_tuning/jobs \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"training_file": "file-abc123",
"model": "gpt-4o-mini",
"method": {
"type": "reinforcement",
"reinforcement": {
"grader": {
"type": "lambda",
"lambda": {
"function": "arn:aws:lambda:us-west-2:123456789012:function:my-grader"
}
},
"hyperparameters": {
"n_epochs": 1,
"batch_size": 4,
"learning_rate_multiplier": 1.0
}
}
}
}'
列出微调事件
列出微调任务的事件。微调事件提供有关任务进度的详细信息，包括训练指标、检查点创建和错误消息。如需完整的 API 详细信息，请参阅OpenAI列表微调事件文档。
示例
要列出微调事件，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
# List fine-tuning events
events = client.fine_tuning.jobs.list_events(
fine_tuning_job_id="ftjob-abc123",
limit=50
)
for event in events.data:
print(f"[{event.created_at}] {event.level}: {event.message}")
if event.data:
print(f" Metrics: {event.data}")
- HTTP request
-
向以下地址发出 GET 请求/v1/fine_tuning/jobs/{fine_tuning_job_id}/events：
curl https://bedrock-mantle.us-west-2.api.aws/v1/fine_tuning/jobs/ftjob-abc123/events?limit=50
活动包括以下信息：
-
培训开始和完成消息
-
检查点创建通知
-
每个步骤的训练指标（损失、准确性）
-
任务失败时的错误消息
要对所有事件进行分页，请选择首选方法对应的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
# Paginate through all events
all_events = []
after = None
while True:
events = client.fine_tuning.jobs.list_events(
fine_tuning_job_id="ftjob-abc123",
limit=100,
after=after
)
all_events.extend(events.data)
if not events.has_more:
break
after = events.data[-1].id
- HTTP request
-
使用以下after参数发出多个 GET 请求：
# First request
curl https://bedrock-mantle.us-west-2.api.aws/v1/fine_tuning/jobs/ftjob-abc123/events?limit=100
# Subsequent requests with 'after' parameter
curl "https://bedrock-mantle.us-west-2.api.aws/v1/fine_tuning/jobs/ftjob-abc123/events?limit=100&after=ft-event-abc123"
检索微调作业
获取有关微调任务的详细信息。如需完整的 API 详细信息，请参阅OpenAI检索微调任务文档。
示例
要检索特定的工作详细信息，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
# Retrieve specific job details
job_details = client.fine_tuning.jobs.retrieve(job_id)
# Print raw response
print(json.dumps(job_details.model_dump(), indent=2))
- HTTP request
-
向以下地址发出 GET 请求/v1/fine_tuning/jobs/{fine_tuning_job_id}：
curl https://bedrock-mantle.us-west-2.api.aws/v1/fine_tuning/jobs/ftjob-abc123 \
-H "Authorization: Bearer $OPENAI_API_KEY"
列出微调任务
列出贵组织支持分页的微调任务。如需完整的 API 详细信息，请参阅OpenAI列出微调任务文档。
示例
要列出带有限制和分页的微调作业，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
# List fine-tuning jobs with limit and pagination
response = client.fine_tuning.jobs.list(
limit=20 # Maximum number of jobs to return
)
# Print raw response
print(json.dumps(response.model_dump(), indent=2))
- HTTP request
-
向以下地址发出 GET 请求/v1/fine_tuning/jobs：
curl https://bedrock-mantle.us-west-2.api.aws/v1/fine_tuning/jobs?limit=20 \
-H "Authorization: Bearer $OPENAI_API_KEY"
取消微调作业
取消正在进行的微调任务。一旦取消，任务将无法恢复。如需完整的 API 详细信息，请参阅OpenAI取消微调任务文档。
示例
要取消微调作业，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
# Cancel fine-tuning job
cancel_response = client.fine_tuning.jobs.cancel("ftjob-abc123")
print(f"Job ID: {cancel_response.id}")
print(f"Status: {cancel_response.status}") # Should be "cancelled"
- HTTP request
-
向以下地址发出 POST 请求/v1/fine_tuning/jobs/{fine_tuning_job_id}/cancel：
curl -X POST https://bedrock-mantle.us-west-2.api.aws/v1/fine_tuning/jobs/ftjob-abc123/cancel \
-H "Authorization: Bearer $OPENAI_API_KEY"
列出微调检查点
列出微调作业的检查点。检查点是在微调期间创建的中间模型快照，可用于推断，以评估不同训练阶段的性能。有关更多信息，请参阅 OpenAIList 微调检查点文档。
示例
要列出微调作业的检查点，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
# List checkpoints for a fine-tuning job
checkpoints = client.fine_tuning.jobs.checkpoints.list(
fine_tuning_job_id="ftjob-abc123",
limit=10
)
for checkpoint in checkpoints.data:
print(f"Checkpoint ID: {checkpoint.id}")
print(f"Step: {checkpoint.step_number}")
print(f"Model: {checkpoint.fine_tuned_model_checkpoint}")
print(f"Metrics: {checkpoint.metrics}")
print("---")
- HTTP request
-
向以下地址发出 GET 请求/v1/fine_tuning/jobs/{fine_tuning_job_id}/checkpoints：
curl https://bedrock-mantle.us-west-2.api.aws/v1/fine_tuning/jobs/ftjob-abc123/checkpoints?limit=10
每个检查点包括：
-
检查点 ID-检查点的唯一标识符
-
步骤编号-创建检查点的训练步骤
-
模型检查点-可用于推理的模型标识符
-
指标-此检查点的验证损失和准确性
要使用检查点模型进行推理，请选择首选方法的选项卡，然后按照以下步骤操作：
- OpenAI SDK (Python)
-
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
# Test inference with a checkpoint
response = client.chat.completions.create(
model=checkpoint.fine_tuned_model_checkpoint,
messages=[{"role": "user", "content": "What is AI?"}],
max_tokens=100
)
print(response.choices[0].message.content)
- HTTP request
-
向以下地址发出 POST 请求/v1/chat/completions：
curl https://bedrock-mantle.us-west-2.api.aws/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "ft:gpt-4o-mini:openai:custom:7p4lURel:ckpt-step-1000",
"messages": [{"role": "user", "content": "What is AI?"}],
"max_tokens": 100
}'
使用经过微调的模型运行推理
微调任务完成后，您可以使用经过微调的模型通过响应 API 或聊天完成 API 进行推理。有关 API 的完整详细信息，请参阅使用生成响应 OpenAI APIs。
响应 API
在经过微调的模型中使用 Responses API 生成单圈文本：
- OpenAI SDK (Python)
-
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
# Get the fine-tuned model ID
job_details = client.fine_tuning.jobs.retrieve("ftjob-abc123")
if job_details.status == 'succeeded' and job_details.fine_tuned_model:
fine_tuned_model = job_details.fine_tuned_model
print(f"Using fine-tuned model: {fine_tuned_model}")
# Run inference with Responses API
response = client.completions.create(
model=fine_tuned_model,
prompt="What is the capital of France?",
max_tokens=100,
temperature=0.7
)
print(f"Response: {response.choices[0].text}")
else:
print(f"Job status: {job_details.status}")
print("Job must be in 'succeeded' status to run inference")
- HTTP request
-
向以下地址发出 POST 请求/v1/completions：
curl https://bedrock-mantle.us-west-2.api.aws/v1/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "ft:gpt-4o-mini:openai:custom-model:7p4lURel",
"prompt": "What is the capital of France?",
"max_tokens": 100,
"temperature": 0.7
}'
聊天完成 API
使用 Chat Completions API 与经过微调的模型进行对话互动：
- OpenAI SDK (Python)
-
# Requires OPENAI_API_KEY and OPENAI_BASE_URL environment variables
from openai import OpenAI
client = OpenAI()
# Get the fine-tuned model ID
job_details = client.fine_tuning.jobs.retrieve("ftjob-abc123")
if job_details.status == 'succeeded' and job_details.fine_tuned_model:
fine_tuned_model = job_details.fine_tuned_model
print(f"Using fine-tuned model: {fine_tuned_model}")
# Run inference
inference_response = client.chat.completions.create(
model=fine_tuned_model,
messages=[
{"role": "user", "content": "What is the capital of France?"}
],
max_tokens=100
)
print(f"Response: {inference_response.choices[0].message.content}")
else:
print(f"Job status: {job_details.status}")
print("Job must be in 'succeeded' status to run inference")
- HTTP request
-
向以下地址发出 POST 请求/v1/chat/completions：
curl https://bedrock-mantle.us-west-2.api.aws/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "ft:gpt-4o-mini:openai:custom-model:7p4lURel",
"messages": [
{"role": "user", "content": "What is the capital of France?"}
],
"max_tokens": 100
}'
